Which solution will meet these requirements?
Create an AWS Lambda function that consolidates each day’s AWS WAF logs into one log file.
Reduce the amount of data scanned by configuring AWS WAF to send logs to a different S3 bucket each day.
Update the Kinesis Data Firehose configuration to partition the data in Amazon S3 by date and time. Create external tables for Amazon Redshift. Configure Amazon Redshift Spectrum to query the data source.
Modify the Kinesis Data Firehose configuration and Athena table definition to partition the data by date and time. Change the Athena query to view the relevant partitions.
Explanations:
Consolidating logs into a single file for each day may reduce the number of files but will not improve query performance significantly. This approach does not address the issue of optimizing query time in Athena.
Changing the S3 bucket each day does not reduce the amount of data scanned or optimize query performance. The query still scans the entire dataset, so performance improvements are unlikely.
Using Amazon Redshift Spectrum to query the data from S3 does not directly address the issue with Athena queries. It introduces unnecessary complexity and does not optimize Athena’s performance.
Partitioning the data by date and time in S3 allows Athena to scan only relevant partitions, reducing the amount of data processed and improving query performance. Updating the Athena query to target specific partitions further optimizes query times.
I evaluate that the answer is:
Modify the Kinesis Data Firehose configuration and Athena table definition to partition the data by date and time. Change the Athena query to view the relevant partitions.
I opine that the answer is:
Modify the Kinesis Data Firehose configuration and Athena table definition to partition the data by date and time. Change the Athena query to view the relevant partitions.